AugLiChem: Data Augmentation Library of Chemical Structures for Machine Learning

TL;DR

AugLiChem is a Python library that applies data augmentation techniques to chemical structures, significantly enhancing machine learning model performance for property prediction in molecules and crystalline materials.

Contribution

The paper introduces AugLiChem, a novel data augmentation library for chemical structures that improves ML model accuracy, especially with Graph Neural Networks, by providing plug-in augmentation methods.

Findings

Augmentation strategies improve ML model performance on chemical data.

Significant gains observed with GNNs using AugLiChem.

The library is publicly available for use and integration.

Abstract

Machine learning (ML) has demonstrated the promise for accurate and efficient property prediction of molecules and crystalline materials. To develop highly accurate ML models for chemical structure property prediction, datasets with sufficient samples are required. However, obtaining clean and sufficient data of chemical properties can be expensive and time-consuming, which greatly limits the performance of ML models. Inspired by the success of data augmentations in computer vision and natural language processing, we developed AugLiChem: the data augmentation library for chemical structures. Augmentation methods for both crystalline systems and molecules are introduced, which can be utilized for fingerprint-based ML models and Graph Neural Networks(GNNs). We show that using our augmentation strategies significantly improves the performance of ML models, especially when using GNNs. In addition, the augmentations that we developed can be used as a direct plug-in module during training and have demonstrated the effectiveness when implemented with different GNN models through the AugliChem library. The Python-based package for our implementation of Auglichem: Data augmentation library for chemical structures, is publicly available at: https://github.com/BaratiLab/AugLiChem.